Python、索引和Pandas数据帧

Question

我是Python新手，有一个关于索引和数据帧的问题。我有3个源文件，可以用三个键唯一地标识（地区代码，地区名称，地区类型）。我正在验证源文件和数据库中的数据。下面的代码只在源文件和数据库之间查找匹配的dist_name和dist_代码，然后根据可用的值将它们进行匹配。我需要向函数添加另一个条件，添加一个额外的索引/键（district_type），它将使dist唯一地匹配和比较所有三个键。在

*编辑** 为了简化这一点，我改变了方法中的逻辑。通过连接两个键“地区代码”和“地区”类型，我有一个唯一的标识符（dist_key）。我改变了下面的函数来反映这个变化，但是我收到了一个“keyError:u'no item named dist_key”。这个函数产生了一个错误，因为我相信新的唯一标识符只在这个函数中定义。作为这种语言和脚本的新手，我不知道如何在函数之外调用所需的变量（dist_key）。在

if dfyearfound:
    df2['district_name']=df2['district_name'].str.strip()
    df2['district_code']=df2['district_code'].str.strip()
    **df2['dist_key']=df2['dist_key'].str.strip()**  """This line is causing the error"""

def addNamesCodes(testframe,districtnamedata,districtcodedata):
        """ Function that will correct any missing data such as district names or district codes.  Parameter is a pandas dataframe, dictionaries which map the district names and district codes """


        #contain list of correct district codes and district names
        districtnames=[]
        districtkeys=[]
        #Non matches
        fdistrictnames=[]
        fdistrictkeys=[]
        #fill empty values in names and codes

        testframe['district_name']=testframe['district_name'].apply(lambda x: str(x))
        testframe['district_name']=testframe['district_name'].fillna('')
        testframe['district_code']=testframe['district_code'].fillna('')
        testframe['dist_key']=testframe['dist_key'].fillna('')
        testframe['dist_key']=testframe['dist_key']+testframe['district_code']
        #Create two new columns containing the district names and district codes in same format as enrollment and teacher data  
        for i in range(len(testframe.index)):
            #both district code and district name are present
            if districtnamedata.has_key(testframe['dist_key'][testframe.index[i]]) and districtcodedata.has_key(testframe['district_name'][testframe.index[i]]):
                #district code and district name are a match
                if ((districtnamedata[testframe['dist_key'][testframe.index[i]]]==testframe['district_name'][testframe.index[i]]) and (districtcodedata[testframe['district_name'][testframe.index[i]]]==testframe['dist_key'][testframe.index[i]])):
                    districtnames.append(districtnamedata[testframe['dist_key'][testframe.index[i]]])
                    districtkeys.append(districtcodedata[testframe['district_name'][testframe.index[i]]])
                #potential wrong mappings
                else:
                    districtkeys.append(testframe['dist_key'][testframe.index[i]])
                    districtnames.append(districtnamedata[testframe['dist_key'][testframe.index[i]]])
            else:
                #check if district code is present
                if districtnamedata.has_key(testframe['dist_key'][testframe.index[i]]):
                    districtkeys.append(testframe['dist_key'][testframe.index[i]])
                    districtnames.append(districtnamedata[testframe['dist_key'][testframe.index[i]]])
                #check if only district name is present 
                elif districtcodedata.has_key(testframe['district_name'][testframe.index[i]]):
                    districtnames.append(testframe['district_name'][testframe.index[i]])
                    districtkeys.append(districtcodedata[testframe['district_name'][testframe.index[i]]])
                #complete nonmatches
                else:
                    fdistrictnames.append(testframe['district_name'][testframe.index[i]])
                    fdistrictkeys.append(testframe['dist_key'][testframe.index[i]])
        #extend the list by the complete nonmatches
        districtnames.extend(fdistrictnames)
        districtkeys.extend(fdistrictkeys)

        return districtnames,districtkeys

样品来源： 注册---

^{pr2}$

金融--

district_code   district_name   district_type_code          budget
1                 AITKIN               1                    122000
1                 AITKIN               1                    120003
1                 SAVAGE               3                    140000
1                 SAVAGE               3                    780000
15              ST. FRANCIS            1                    782000
16              SPRING LAKE            1                    784000

老师--

district_code   district_name   district_type_code         Salary
1                 AITKIN               1                    50000
1                 AITKIN               1                    42000
1                 SAVAGE               3                    89000
1                 SAVAGE               3                    32000
15              ST. FRANCIS            1                    78000
16              SPRING LAKE            1                    58000